You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

RNNs for modeling sequences

In this section, now that we understand sequences, we can look at the foundations of RNNs. We'll start by introducing the typical structure of an RNN, and we'll see how the data flows through it with one or more hidden layers. We'll then examine how the neuron activations are computed in a typical RNN. This will create a context for us to discuss the common challenges in training RNNs, and explore the modern solution to these challenges—LSTM.

Understanding the structure and flow of an RNN

Let's start by introducing the architecture of an RNN. The following figure shows a standard feedforward neural network and an RNN, in a side by side for comparison:

Both of these networks have only one hidden layer. In this representation, the units are not displayed, but we assume that the input layer (x), hidden layer (h), and output layer (y) are vectors which contain many units.